Dashbell: A Low-cost Smart Doorbell System for Home Use

Smart doorbells allow home owners to receive alerts when a visitor is at the door, see who the guest is, and communicate with the visitor from a smart device. They greatly improve people's life quality and contribute to the evolution of smart homes. However, the commercial smart doorbells are quite expensive, usually cost more than 190 US dollars, which is a substantial impediment on the pervasiveness of smart doorbells. To solve this problem, we introduce the Dashbell-a budget smart doorbell system for home use. It connects a WiFi-enabled device, the Amazon Dash Button, to a network and enables the home owner to answer the bell triggered by the dash button using a smartphone. The Dashbell system also enables fast fault detection and diagnosis due to its distributed framework.Comment: Accepted by IEEE PerCom 201

Counteracting the Negative Effect of Form Auto-completion on the Privacy Calculus

When filling out web forms, people typically do not want to submit every piece of requested information to every website. Instead, they selectively disclose information after weighing the potential benefits and risks of disclosure: a process called “privacy calculus”. Giving users control over what to enter is a prerequisite for this selective disclosure behavior. Exercising this control by manually filling out a form is a burden though. Modern browsers therefore offer an auto-completion feature that automatically fills out forms with previously stored values. This feature is convenient, but it makes it so easy to submit a fully completed form that users seem to skip the privacy calculus altogether. In an experiment we compare this traditional auto-completion tool with two alternative tools that give users more control than the traditional tool. While users of the traditional tool indeed forego their selective disclosure behavior, the alternative tools effectively reinstate the privacy calculus

Learning about Users from Observation

Many approaches and systems for recommending information, goods, or other kinds of objects have been developed in recent years. In these systems, machine learning methods are often used that need training input to acquire a user interest profile. Such methods typically need positive and negative evidence of the user’s interests. To obtain both kinds of evidence, many systems make users rate relevant objects explicitly. Others merely observe the user’s behavior, which yields positive evidence only; in order to be able to apply the standard learning methods, these systems mostly use heuristics to also find negative evidence in observed behavior. In this paper, we present an approach for learning interest profiles from positive evidence only, as it is contained in observed user behavior. Thus, both the problem of interrupting the user for ratings and the problem of somewhat artificially determining negative evidence are avoided. A methodology for learning explicit user profiles and recommending interesting objects has been developed. It is used in the context of ELFI – a Web-based information system. The evaluation results are briefly described in this paper. Our current efforts revolve around further improvements of the methodology and its implementation for recommending interesting web pages to users of a web browser

Human Overpopulation

https://digitalcommons.wpi.edu/gps-posters/1650/thumbnail.jp

DIFFERENCES IN ONLINE PRIVACY & SECURITY ATTITUDES BASED ON ECONOMIC LIVING STANDARDS: A GLOBAL STUDY OF 24 COUNTRIES

This work explores online privacy and security attitudes from 24,143 individuals across 24 countries with diverse economic living standards. By using k-mode analysis, we identified three distinct profiles based on similarity in Internet security and privacy attitudes measured by 83 items. By comparing the aggregated dissimilarity measures between each respondent and the centroid values of the three profiles at the country level, we assigned each country to their best-fitting privacy profile. We found significant differences in GDP per capita between profiles 1 (highest GDP) to 3 (lowest). People in profiles with higher GDP per capita have significantly greater privacy concerns in relation to information being monitored or bought and sold. These individuals are also more reluctant towards government surveillance of online communication as well as less likely to agree that governments should work with other public and private entities to develop online security laws. As economic living standards improve, the proportion of individuals increases in profile 1, decreases in profile 2, and most rapidly drops in profile 3. To the best of our knowledge, it is the first research that systematically examines country-level privacy in relation to a national economic variable using GDP per capita